Giant Thermal Amplification via Engineered Dissipation in a Sierpinski-Gasket Aharonov-Bohm Interferometer

What the math gives to ML

The paper offers a concrete interference mechanism: an engineered dissipative third terminal can cancel the differential response of the base heat current while emitter and collector currents remain finite, producing a large response ratio without relying on a resonance. The key quantitative object is the Onsager ratio \(\alpha=|M_{24}/M_{44}|\), which diverges when the base susceptibility \(M_{44}\) crosses zero. A transferable neural-network analogue is a three-branch module with a noisy or damped auxiliary branch and a tunable signed mixing phase, trained to create a controlled zero in the auxiliary branch's response to a designated perturbation while preserving the task-gradient response. This should be treated as a response-shaping and robustness mechanism, not as an unconstrained objective, because exact cancellation can make the ratio ill-conditioned.